Overview

Dataset statistics

Number of variables21
Number of observations9800
Missing cells11
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory168.0 B

Variable types

Numeric6
Text6
DateTime2
Categorical7

Alerts

Country has constant value ""Constant
Row ID is uniformly distributedUniform
Row ID has unique valuesUnique
Discount has 4691 (47.9%) zerosZeros

Reproduction

Analysis started2024-01-19 02:52:26.567603
Analysis finished2024-01-19 02:52:35.576568
Duration9.01 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Row ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct9800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4900.5
Minimum1
Maximum9800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:35.728918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile490.95
Q12450.75
median4900.5
Q37350.25
95-th percentile9310.05
Maximum9800
Range9799
Interquartile range (IQR)4899.5

Descriptive statistics

Standard deviation2829.1607
Coefficient of variation (CV)0.57732081
Kurtosis-1.2
Mean4900.5
Median Absolute Deviation (MAD)2450
Skewness0
Sum48024900
Variance8004150
MonotonicityStrictly increasing
2024-01-19T09:52:35.902260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6537 1
 
< 0.1%
6530 1
 
< 0.1%
6531 1
 
< 0.1%
6532 1
 
< 0.1%
6533 1
 
< 0.1%
6534 1
 
< 0.1%
6535 1
 
< 0.1%
6536 1
 
< 0.1%
6538 1
 
< 0.1%
Other values (9790) 9790
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
9800 1
< 0.1%
9799 1
< 0.1%
9798 1
< 0.1%
9797 1
< 0.1%
9796 1
< 0.1%
9795 1
< 0.1%
9794 1
< 0.1%
9793 1
< 0.1%
9792 1
< 0.1%
9791 1
< 0.1%
Distinct4922
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:36.106777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters137200
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2499 ?
Unique (%)25.5%

Sample

1st rowCA-2017-152156
2nd rowCA-2017-152156
3rd rowCA-2017-138688
4th rowUS-2016-108966
5th rowUS-2016-108966
ValueCountFrequency (%)
ca-2018-100111 14
 
0.1%
ca-2018-157987 12
 
0.1%
ca-2017-165330 11
 
0.1%
us-2017-108504 11
 
0.1%
ca-2017-105732 10
 
0.1%
ca-2016-131338 10
 
0.1%
us-2016-126977 10
 
0.1%
ca-2016-158421 9
 
0.1%
ca-2016-132626 9
 
0.1%
ca-2017-145177 9
 
0.1%
Other values (4912) 9695
98.9%
2024-01-19T09:52:36.534476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 25023
18.2%
- 19600
14.3%
0 15209
11.1%
2 15047
11.0%
C 8161
 
5.9%
A 8161
 
5.9%
6 7282
 
5.3%
8 7234
 
5.3%
5 7084
 
5.2%
7 6568
 
4.8%
Other values (5) 17831
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98000
71.4%
Dash Punctuation 19600
 
14.3%
Uppercase Letter 19600
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25023
25.5%
0 15209
15.5%
2 15047
15.4%
6 7282
 
7.4%
8 7234
 
7.4%
5 7084
 
7.2%
7 6568
 
6.7%
3 5352
 
5.5%
4 5300
 
5.4%
9 3901
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 8161
41.6%
A 8161
41.6%
U 1639
 
8.4%
S 1639
 
8.4%
Dash Punctuation
ValueCountFrequency (%)
- 19600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 117600
85.7%
Latin 19600
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25023
21.3%
- 19600
16.7%
0 15209
12.9%
2 15047
12.8%
6 7282
 
6.2%
8 7234
 
6.2%
5 7084
 
6.0%
7 6568
 
5.6%
3 5352
 
4.6%
4 5300
 
4.5%
Latin
ValueCountFrequency (%)
C 8161
41.6%
A 8161
41.6%
U 1639
 
8.4%
S 1639
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25023
18.2%
- 19600
14.3%
0 15209
11.1%
2 15047
11.0%
C 8161
 
5.9%
A 8161
 
5.9%
6 7282
 
5.3%
8 7234
 
5.3%
5 7084
 
5.2%
7 6568
 
4.8%
Other values (5) 17831
13.0%
Distinct1230
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
Minimum2015-01-02 00:00:00
Maximum2018-12-30 00:00:00
2024-01-19T09:52:36.731581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:37.036928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1326
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
Minimum2015-01-04 00:00:00
Maximum2019-05-01 00:00:00
2024-01-19T09:52:37.225178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:37.433291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ship Mode
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
Standard Class
5859 
Second Class
1902 
First Class
1501 
Same Day
 
538

Length

Max length14
Median length14
Mean length12.822959
Min length8

Characters and Unicode

Total characters125665
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecond Class
2nd rowSecond Class
3rd rowSecond Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 5859
59.8%
Second Class 1902
 
19.4%
First Class 1501
 
15.3%
Same Day 538
 
5.5%

Length

2024-01-19T09:52:37.622647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-19T09:52:37.796898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
class 9262
47.3%
standard 5859
29.9%
second 1902
 
9.7%
first 1501
 
7.7%
same 538
 
2.7%
day 538
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 22056
17.6%
s 20025
15.9%
d 13620
10.8%
9800
7.8%
l 9262
7.4%
C 9262
7.4%
S 8299
 
6.6%
n 7761
 
6.2%
r 7360
 
5.9%
t 7360
 
5.9%
Other values (8) 10860
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96265
76.6%
Uppercase Letter 19600
 
15.6%
Space Separator 9800
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22056
22.9%
s 20025
20.8%
d 13620
14.1%
l 9262
9.6%
n 7761
 
8.1%
r 7360
 
7.6%
t 7360
 
7.6%
e 2440
 
2.5%
c 1902
 
2.0%
o 1902
 
2.0%
Other values (3) 2577
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 9262
47.3%
S 8299
42.3%
F 1501
 
7.7%
D 538
 
2.7%
Space Separator
ValueCountFrequency (%)
9800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 115865
92.2%
Common 9800
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22056
19.0%
s 20025
17.3%
d 13620
11.8%
l 9262
8.0%
C 9262
8.0%
S 8299
 
7.2%
n 7761
 
6.7%
r 7360
 
6.4%
t 7360
 
6.4%
e 2440
 
2.1%
Other values (7) 8420
 
7.3%
Common
ValueCountFrequency (%)
9800
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22056
17.6%
s 20025
15.9%
d 13620
10.8%
9800
7.8%
l 9262
7.4%
C 9262
7.4%
S 8299
 
6.6%
n 7761
 
6.2%
r 7360
 
5.9%
t 7360
 
5.9%
Other values (8) 10860
8.6%
Distinct793
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:38.002960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters78400
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowCG-12520
2nd rowCG-12520
3rd rowDV-13045
4th rowSO-20335
5th rowSO-20335
ValueCountFrequency (%)
wb-21850 35
 
0.4%
pp-18955 34
 
0.3%
ma-17560 34
 
0.3%
jl-15835 33
 
0.3%
ck-12205 32
 
0.3%
jd-15895 32
 
0.3%
sv-20365 32
 
0.3%
ep-13915 31
 
0.3%
zc-21910 31
 
0.3%
ap-10915 31
 
0.3%
Other values (783) 9475
96.7%
2024-01-19T09:52:38.442748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11696
14.9%
- 9800
12.5%
0 8365
 
10.7%
5 7709
 
9.8%
2 4607
 
5.9%
7 2863
 
3.7%
6 2845
 
3.6%
9 2836
 
3.6%
8 2777
 
3.5%
3 2738
 
3.5%
Other values (30) 22164
28.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49000
62.5%
Uppercase Letter 19557
 
24.9%
Dash Punctuation 9800
 
12.5%
Lowercase Letter 43
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 1773
 
9.1%
C 1701
 
8.7%
M 1687
 
8.6%
B 1613
 
8.2%
D 1258
 
6.4%
A 1196
 
6.1%
J 1120
 
5.7%
P 1082
 
5.5%
H 927
 
4.7%
K 912
 
4.7%
Other values (16) 6288
32.2%
Decimal Number
ValueCountFrequency (%)
1 11696
23.9%
0 8365
17.1%
5 7709
15.7%
2 4607
 
9.4%
7 2863
 
5.8%
6 2845
 
5.8%
9 2836
 
5.8%
8 2777
 
5.7%
3 2738
 
5.6%
4 2564
 
5.2%
Lowercase Letter
ValueCountFrequency (%)
p 29
67.4%
o 8
 
18.6%
l 6
 
14.0%
Dash Punctuation
ValueCountFrequency (%)
- 9800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58800
75.0%
Latin 19600
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1773
 
9.0%
C 1701
 
8.7%
M 1687
 
8.6%
B 1613
 
8.2%
D 1258
 
6.4%
A 1196
 
6.1%
J 1120
 
5.7%
P 1082
 
5.5%
H 927
 
4.7%
K 912
 
4.7%
Other values (19) 6331
32.3%
Common
ValueCountFrequency (%)
1 11696
19.9%
- 9800
16.7%
0 8365
14.2%
5 7709
13.1%
2 4607
 
7.8%
7 2863
 
4.9%
6 2845
 
4.8%
9 2836
 
4.8%
8 2777
 
4.7%
3 2738
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11696
14.9%
- 9800
12.5%
0 8365
 
10.7%
5 7709
 
9.8%
2 4607
 
5.9%
7 2863
 
3.7%
6 2845
 
3.6%
9 2836
 
3.6%
8 2777
 
3.5%
3 2738
 
3.5%
Other values (30) 22164
28.3%
Distinct793
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:38.711178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length12.962959
Min length7

Characters and Unicode

Total characters127037
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowClaire Gute
2nd rowClaire Gute
3rd rowDarrin Van Huff
4th rowSean O'Donnell
5th rowSean O'Donnell
ValueCountFrequency (%)
michael 119
 
0.6%
frank 112
 
0.6%
john 106
 
0.5%
patrick 96
 
0.5%
stewart 93
 
0.5%
paul 92
 
0.5%
rick 91
 
0.5%
brian 88
 
0.4%
matt 86
 
0.4%
ken 85
 
0.4%
Other values (901) 18697
95.1%
2024-01-19T09:52:39.133678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11750
 
9.2%
e 11605
 
9.1%
n 10005
 
7.9%
9865
 
7.8%
r 9318
 
7.3%
i 7797
 
6.1%
l 6389
 
5.0%
o 5722
 
4.5%
t 5376
 
4.2%
s 4454
 
3.5%
Other values (47) 44756
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96951
76.3%
Uppercase Letter 20068
 
15.8%
Space Separator 9865
 
7.8%
Other Punctuation 124
 
0.1%
Dash Punctuation 29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11750
12.1%
e 11605
12.0%
n 10005
10.3%
r 9318
9.6%
i 7797
 
8.0%
l 6389
 
6.6%
o 5722
 
5.9%
t 5376
 
5.5%
s 4454
 
4.6%
h 3787
 
3.9%
Other values (18) 20748
21.4%
Uppercase Letter
ValueCountFrequency (%)
C 1806
 
9.0%
S 1773
 
8.8%
M 1724
 
8.6%
B 1667
 
8.3%
D 1287
 
6.4%
A 1251
 
6.2%
J 1120
 
5.6%
P 1082
 
5.4%
H 964
 
4.8%
K 944
 
4.7%
Other values (16) 6450
32.1%
Space Separator
ValueCountFrequency (%)
9865
100.0%
Other Punctuation
ValueCountFrequency (%)
' 124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 117019
92.1%
Common 10018
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11750
 
10.0%
e 11605
 
9.9%
n 10005
 
8.5%
r 9318
 
8.0%
i 7797
 
6.7%
l 6389
 
5.5%
o 5722
 
4.9%
t 5376
 
4.6%
s 4454
 
3.8%
h 3787
 
3.2%
Other values (44) 40816
34.9%
Common
ValueCountFrequency (%)
9865
98.5%
' 124
 
1.2%
- 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126949
99.9%
None 88
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11750
 
9.3%
e 11605
 
9.1%
n 10005
 
7.9%
9865
 
7.8%
r 9318
 
7.3%
i 7797
 
6.1%
l 6389
 
5.0%
o 5722
 
4.5%
t 5376
 
4.2%
s 4454
 
3.5%
Other values (44) 44668
35.2%
None
ValueCountFrequency (%)
ö 60
68.2%
ä 23
 
26.1%
ü 5
 
5.7%

Segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
Consumer
5101 
Corporate
2953 
Home Office
1746 

Length

Max length11
Median length8
Mean length8.8358163
Min length8

Characters and Unicode

Total characters86591
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowCorporate
4th rowConsumer
5th rowConsumer

Common Values

ValueCountFrequency (%)
Consumer 5101
52.1%
Corporate 2953
30.1%
Home Office 1746
 
17.8%

Length

2024-01-19T09:52:39.336855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-19T09:52:39.494688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
consumer 5101
44.2%
corporate 2953
25.6%
home 1746
 
15.1%
office 1746
 
15.1%

Most occurring characters

ValueCountFrequency (%)
o 12753
14.7%
e 11546
13.3%
r 11007
12.7%
C 8054
9.3%
m 6847
7.9%
n 5101
 
5.9%
s 5101
 
5.9%
u 5101
 
5.9%
f 3492
 
4.0%
t 2953
 
3.4%
Other values (7) 14636
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73299
84.6%
Uppercase Letter 11546
 
13.3%
Space Separator 1746
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 12753
17.4%
e 11546
15.8%
r 11007
15.0%
m 6847
9.3%
n 5101
 
7.0%
s 5101
 
7.0%
u 5101
 
7.0%
f 3492
 
4.8%
t 2953
 
4.0%
p 2953
 
4.0%
Other values (3) 6445
8.8%
Uppercase Letter
ValueCountFrequency (%)
C 8054
69.8%
H 1746
 
15.1%
O 1746
 
15.1%
Space Separator
ValueCountFrequency (%)
1746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84845
98.0%
Common 1746
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 12753
15.0%
e 11546
13.6%
r 11007
13.0%
C 8054
9.5%
m 6847
8.1%
n 5101
 
6.0%
s 5101
 
6.0%
u 5101
 
6.0%
f 3492
 
4.1%
t 2953
 
3.5%
Other values (6) 12890
15.2%
Common
ValueCountFrequency (%)
1746
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 12753
14.7%
e 11546
13.3%
r 11007
12.7%
C 8054
9.3%
m 6847
7.9%
n 5101
 
5.9%
s 5101
 
5.9%
u 5101
 
5.9%
f 3492
 
4.0%
t 2953
 
3.4%
Other values (7) 14636
16.9%

Country
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
United States
9800 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters127400
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States 9800
100.0%

Length

2024-01-19T09:52:39.667964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-19T09:52:39.859907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
united 9800
50.0%
states 9800
50.0%

Most occurring characters

ValueCountFrequency (%)
t 29400
23.1%
e 19600
15.4%
U 9800
 
7.7%
n 9800
 
7.7%
i 9800
 
7.7%
d 9800
 
7.7%
9800
 
7.7%
S 9800
 
7.7%
a 9800
 
7.7%
s 9800
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98000
76.9%
Uppercase Letter 19600
 
15.4%
Space Separator 9800
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 29400
30.0%
e 19600
20.0%
n 9800
 
10.0%
i 9800
 
10.0%
d 9800
 
10.0%
a 9800
 
10.0%
s 9800
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
U 9800
50.0%
S 9800
50.0%
Space Separator
ValueCountFrequency (%)
9800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 117600
92.3%
Common 9800
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 29400
25.0%
e 19600
16.7%
U 9800
 
8.3%
n 9800
 
8.3%
i 9800
 
8.3%
d 9800
 
8.3%
S 9800
 
8.3%
a 9800
 
8.3%
s 9800
 
8.3%
Common
ValueCountFrequency (%)
9800
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 29400
23.1%
e 19600
15.4%
U 9800
 
7.7%
n 9800
 
7.7%
i 9800
 
7.7%
d 9800
 
7.7%
9800
 
7.7%
S 9800
 
7.7%
a 9800
 
7.7%
s 9800
 
7.7%

City
Text

Distinct529
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:40.098778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.3178571
Min length4

Characters and Unicode

Total characters91315
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)0.7%

Sample

1st rowHenderson
2nd rowHenderson
3rd rowLos Angeles
4th rowFort Lauderdale
5th rowFort Lauderdale
ValueCountFrequency (%)
city 970
 
7.0%
new 910
 
6.5%
york 896
 
6.4%
san 792
 
5.7%
los 728
 
5.2%
angeles 728
 
5.2%
philadelphia 532
 
3.8%
francisco 500
 
3.6%
seattle 426
 
3.1%
houston 374
 
2.7%
Other values (553) 7071
50.8%
2024-01-19T09:52:40.538209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8540
 
9.4%
a 7422
 
8.1%
o 7363
 
8.1%
i 6101
 
6.7%
n 6063
 
6.6%
l 5896
 
6.5%
s 4608
 
5.0%
r 4365
 
4.8%
t 4357
 
4.8%
4127
 
4.5%
Other values (41) 32473
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73261
80.2%
Uppercase Letter 13927
 
15.3%
Space Separator 4127
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8540
11.7%
a 7422
10.1%
o 7363
10.1%
i 6101
 
8.3%
n 6063
 
8.3%
l 5896
 
8.0%
s 4608
 
6.3%
r 4365
 
6.0%
t 4357
 
5.9%
c 2339
 
3.2%
Other values (16) 16207
22.1%
Uppercase Letter
ValueCountFrequency (%)
C 2042
14.7%
S 1712
12.3%
L 1255
9.0%
A 1212
8.7%
N 1103
7.9%
P 993
 
7.1%
Y 916
 
6.6%
F 777
 
5.6%
D 623
 
4.5%
H 614
 
4.4%
Other values (14) 2680
19.2%
Space Separator
ValueCountFrequency (%)
4127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87188
95.5%
Common 4127
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8540
 
9.8%
a 7422
 
8.5%
o 7363
 
8.4%
i 6101
 
7.0%
n 6063
 
7.0%
l 5896
 
6.8%
s 4608
 
5.3%
r 4365
 
5.0%
t 4357
 
5.0%
c 2339
 
2.7%
Other values (40) 30134
34.6%
Common
ValueCountFrequency (%)
4127
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8540
 
9.4%
a 7422
 
8.1%
o 7363
 
8.1%
i 6101
 
6.7%
n 6063
 
6.6%
l 5896
 
6.5%
s 4608
 
5.0%
r 4365
 
4.8%
t 4357
 
4.8%
4127
 
4.5%
Other values (41) 32473
35.6%

State
Categorical

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
California
1946 
New York
1097 
Texas
973 
Pennsylvania
582 
Washington
504 
Other values (44)
4698 

Length

Max length20
Median length14
Mean length8.4943878
Min length4

Characters and Unicode

Total characters83245
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKentucky
2nd rowKentucky
3rd rowCalifornia
4th rowFlorida
5th rowFlorida

Common Values

ValueCountFrequency (%)
California 1946
19.9%
New York 1097
 
11.2%
Texas 973
 
9.9%
Pennsylvania 582
 
5.9%
Washington 504
 
5.1%
Illinois 483
 
4.9%
Ohio 454
 
4.6%
Florida 373
 
3.8%
Michigan 253
 
2.6%
North Carolina 247
 
2.5%
Other values (39) 2888
29.5%

Length

2024-01-19T09:52:40.726681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 1946
17.0%
new 1283
 
11.2%
york 1097
 
9.6%
texas 973
 
8.5%
pennsylvania 582
 
5.1%
washington 504
 
4.4%
illinois 483
 
4.2%
ohio 454
 
4.0%
florida 373
 
3.3%
carolina 289
 
2.5%
Other values (43) 3486
30.4%

Most occurring characters

ValueCountFrequency (%)
a 10556
12.7%
i 9695
11.6%
n 7958
 
9.6%
o 7167
 
8.6%
r 5420
 
6.5%
e 4961
 
6.0%
l 4725
 
5.7%
s 4556
 
5.5%
C 2506
 
3.0%
f 1956
 
2.3%
Other values (36) 23745
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70115
84.2%
Uppercase Letter 11460
 
13.8%
Space Separator 1670
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10556
15.1%
i 9695
13.8%
n 7958
11.3%
o 7167
10.2%
r 5420
7.7%
e 4961
7.1%
l 4725
6.7%
s 4556
6.5%
f 1956
 
2.8%
h 1876
 
2.7%
Other values (14) 11245
16.0%
Uppercase Letter
ValueCountFrequency (%)
C 2506
21.9%
N 1614
14.1%
T 1156
10.1%
Y 1097
9.6%
M 761
 
6.6%
I 720
 
6.3%
O 642
 
5.6%
W 614
 
5.4%
P 582
 
5.1%
F 373
 
3.3%
Other values (11) 1395
12.2%
Space Separator
ValueCountFrequency (%)
1670
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81575
98.0%
Common 1670
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10556
12.9%
i 9695
11.9%
n 7958
 
9.8%
o 7167
 
8.8%
r 5420
 
6.6%
e 4961
 
6.1%
l 4725
 
5.8%
s 4556
 
5.6%
C 2506
 
3.1%
f 1956
 
2.4%
Other values (35) 22075
27.1%
Common
ValueCountFrequency (%)
1670
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10556
12.7%
i 9695
11.6%
n 7958
 
9.6%
o 7167
 
8.6%
r 5420
 
6.5%
e 4961
 
6.0%
l 4725
 
5.7%
s 4556
 
5.5%
C 2506
 
3.0%
f 1956
 
2.3%
Other values (36) 23745
28.5%

Postal Code
Real number (ℝ)

Distinct626
Distinct (%)6.4%
Missing11
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean55273.322
Minimum1040
Maximum99301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:40.900288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1040
5-th percentile10009
Q123223
median58103
Q390008
95-th percentile98026
Maximum99301
Range98261
Interquartile range (IQR)66785

Descriptive statistics

Standard deviation32041.223
Coefficient of variation (CV)0.57968695
Kurtosis-1.4926761
Mean55273.322
Median Absolute Deviation (MAD)31929
Skewness-0.13129452
Sum5.4107055 × 108
Variance1.02664 × 109
MonotonicityNot monotonic
2024-01-19T09:52:41.121138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10035 253
 
2.6%
10024 225
 
2.3%
10009 220
 
2.2%
94122 195
 
2.0%
10011 193
 
2.0%
94110 166
 
1.7%
98105 165
 
1.7%
19134 160
 
1.6%
90049 150
 
1.5%
98103 149
 
1.5%
Other values (616) 7913
80.7%
ValueCountFrequency (%)
1040 1
 
< 0.1%
1453 6
 
0.1%
1752 2
 
< 0.1%
1810 4
 
< 0.1%
1841 33
0.3%
1852 16
0.2%
1915 3
 
< 0.1%
2038 17
0.2%
2138 6
 
0.1%
2148 3
 
< 0.1%
ValueCountFrequency (%)
99301 6
 
0.1%
99207 7
 
0.1%
98661 5
 
0.1%
98632 3
 
< 0.1%
98502 5
 
0.1%
98270 2
 
< 0.1%
98226 3
 
< 0.1%
98208 1
 
< 0.1%
98198 7
 
0.1%
98115 112
1.1%

Region
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
West
3140 
East
2785 
Central
2277 
South
1598 

Length

Max length7
Median length4
Mean length4.860102
Min length4

Characters and Unicode

Total characters47629
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowWest
4th rowSouth
5th rowSouth

Common Values

ValueCountFrequency (%)
West 3140
32.0%
East 2785
28.4%
Central 2277
23.2%
South 1598
16.3%

Length

2024-01-19T09:52:41.310215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-19T09:52:41.467585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
west 3140
32.0%
east 2785
28.4%
central 2277
23.2%
south 1598
16.3%

Most occurring characters

ValueCountFrequency (%)
t 9800
20.6%
s 5925
12.4%
e 5417
11.4%
a 5062
10.6%
W 3140
 
6.6%
E 2785
 
5.8%
C 2277
 
4.8%
n 2277
 
4.8%
r 2277
 
4.8%
l 2277
 
4.8%
Other values (4) 6392
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37829
79.4%
Uppercase Letter 9800
 
20.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 9800
25.9%
s 5925
15.7%
e 5417
14.3%
a 5062
13.4%
n 2277
 
6.0%
r 2277
 
6.0%
l 2277
 
6.0%
o 1598
 
4.2%
u 1598
 
4.2%
h 1598
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
W 3140
32.0%
E 2785
28.4%
C 2277
23.2%
S 1598
16.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 47629
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 9800
20.6%
s 5925
12.4%
e 5417
11.4%
a 5062
10.6%
W 3140
 
6.6%
E 2785
 
5.8%
C 2277
 
4.8%
n 2277
 
4.8%
r 2277
 
4.8%
l 2277
 
4.8%
Other values (4) 6392
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 9800
20.6%
s 5925
12.4%
e 5417
11.4%
a 5062
10.6%
W 3140
 
6.6%
E 2785
 
5.8%
C 2277
 
4.8%
n 2277
 
4.8%
r 2277
 
4.8%
l 2277
 
4.8%
Other values (4) 6392
13.4%
Distinct1861
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:41.661378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters147000
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)1.0%

Sample

1st rowFUR-BO-10001798
2nd rowFUR-CH-10000454
3rd rowOFF-LA-10000240
4th rowFUR-TA-10000577
5th rowOFF-ST-10000760
ValueCountFrequency (%)
off-pa-10001970 19
 
0.2%
tec-ac-10003832 18
 
0.2%
fur-fu-10004270 16
 
0.2%
tec-ac-10002049 15
 
0.2%
tec-ac-10003628 15
 
0.2%
fur-ch-10002647 15
 
0.2%
fur-fu-10001473 14
 
0.1%
off-pa-10002377 14
 
0.1%
off-bi-10001524 14
 
0.1%
fur-ch-10001146 14
 
0.1%
Other values (1851) 9646
98.4%
2024-01-19T09:52:42.031876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34375
23.4%
- 19600
13.3%
F 15041
10.2%
1 14705
10.0%
O 6201
 
4.2%
2 4773
 
3.2%
4 4737
 
3.2%
3 4721
 
3.2%
A 4338
 
3.0%
5 3340
 
2.3%
Other values (17) 35169
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78400
53.3%
Uppercase Letter 49000
33.3%
Dash Punctuation 19600
 
13.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 15041
30.7%
O 6201
12.7%
A 4338
 
8.9%
C 3242
 
6.6%
U 3193
 
6.5%
T 2959
 
6.0%
R 2863
 
5.8%
P 2673
 
5.5%
E 2061
 
4.2%
B 1718
 
3.5%
Other values (6) 4711
 
9.6%
Decimal Number
ValueCountFrequency (%)
0 34375
43.8%
1 14705
18.8%
2 4773
 
6.1%
4 4737
 
6.0%
3 4721
 
6.0%
5 3340
 
4.3%
7 3032
 
3.9%
9 3000
 
3.8%
6 2924
 
3.7%
8 2793
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 19600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98000
66.7%
Latin 49000
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 15041
30.7%
O 6201
12.7%
A 4338
 
8.9%
C 3242
 
6.6%
U 3193
 
6.5%
T 2959
 
6.0%
R 2863
 
5.8%
P 2673
 
5.5%
E 2061
 
4.2%
B 1718
 
3.5%
Other values (6) 4711
 
9.6%
Common
ValueCountFrequency (%)
0 34375
35.1%
- 19600
20.0%
1 14705
15.0%
2 4773
 
4.9%
4 4737
 
4.8%
3 4721
 
4.8%
5 3340
 
3.4%
7 3032
 
3.1%
9 3000
 
3.1%
6 2924
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34375
23.4%
- 19600
13.3%
F 15041
10.2%
1 14705
10.0%
O 6201
 
4.2%
2 4773
 
3.2%
4 4737
 
3.2%
3 4721
 
3.2%
A 4338
 
3.0%
5 3340
 
2.3%
Other values (17) 35169
23.9%

Category
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
Office Supplies
5909 
Furniture
2078 
Technology
1813 

Length

Max length15
Median length15
Mean length12.802755
Min length9

Characters and Unicode

Total characters125467
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFurniture
2nd rowFurniture
3rd rowOffice Supplies
4th rowFurniture
5th rowOffice Supplies

Common Values

ValueCountFrequency (%)
Office Supplies 5909
60.3%
Furniture 2078
 
21.2%
Technology 1813
 
18.5%

Length

2024-01-19T09:52:42.228198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-19T09:52:42.432595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
office 5909
37.6%
supplies 5909
37.6%
furniture 2078
 
13.2%
technology 1813
 
11.5%

Most occurring characters

ValueCountFrequency (%)
e 15709
12.5%
i 13896
11.1%
p 11818
9.4%
f 11818
9.4%
u 10065
 
8.0%
c 7722
 
6.2%
l 7722
 
6.2%
O 5909
 
4.7%
s 5909
 
4.7%
S 5909
 
4.7%
Other values (10) 28990
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 103849
82.8%
Uppercase Letter 15709
 
12.5%
Space Separator 5909
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15709
15.1%
i 13896
13.4%
p 11818
11.4%
f 11818
11.4%
u 10065
9.7%
c 7722
7.4%
l 7722
7.4%
s 5909
 
5.7%
r 4156
 
4.0%
n 3891
 
3.7%
Other values (5) 11143
10.7%
Uppercase Letter
ValueCountFrequency (%)
O 5909
37.6%
S 5909
37.6%
F 2078
 
13.2%
T 1813
 
11.5%
Space Separator
ValueCountFrequency (%)
5909
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119558
95.3%
Common 5909
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15709
13.1%
i 13896
11.6%
p 11818
9.9%
f 11818
9.9%
u 10065
8.4%
c 7722
 
6.5%
l 7722
 
6.5%
O 5909
 
4.9%
s 5909
 
4.9%
S 5909
 
4.9%
Other values (9) 23081
19.3%
Common
ValueCountFrequency (%)
5909
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 15709
12.5%
i 13896
11.1%
p 11818
9.4%
f 11818
9.4%
u 10065
 
8.0%
c 7722
 
6.2%
l 7722
 
6.2%
O 5909
 
4.7%
s 5909
 
4.7%
S 5909
 
4.7%
Other values (10) 28990
23.1%

Sub-Category
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
Binders
1492 
Paper
1338 
Furnishings
931 
Phones
876 
Storage
832 
Other values (12)
4331 

Length

Max length11
Median length9
Mean length7.1867347
Min length3

Characters and Unicode

Total characters70430
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBookcases
2nd rowChairs
3rd rowLabels
4th rowTables
5th rowStorage

Common Values

ValueCountFrequency (%)
Binders 1492
15.2%
Paper 1338
13.7%
Furnishings 931
9.5%
Phones 876
8.9%
Storage 832
8.5%
Art 785
8.0%
Accessories 756
7.7%
Chairs 607
6.2%
Appliances 459
 
4.7%
Labels 357
 
3.6%
Other values (7) 1367
13.9%

Length

2024-01-19T09:52:42.627450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
binders 1492
15.2%
paper 1338
13.7%
furnishings 931
9.5%
phones 876
8.9%
storage 832
8.5%
art 785
8.0%
accessories 756
7.7%
chairs 607
6.2%
appliances 459
 
4.7%
labels 357
 
3.6%
Other values (7) 1367
13.9%

Most occurring characters

ValueCountFrequency (%)
s 9728
13.8%
e 8695
12.3%
r 7021
 
10.0%
i 5541
 
7.9%
n 5266
 
7.5%
a 4462
 
6.3%
o 3230
 
4.6%
p 2938
 
4.2%
h 2529
 
3.6%
c 2312
 
3.3%
Other values (18) 18708
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60630
86.1%
Uppercase Letter 9800
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 9728
16.0%
e 8695
14.3%
r 7021
11.6%
i 5541
9.1%
n 5266
8.7%
a 4462
7.4%
o 3230
 
5.3%
p 2938
 
4.8%
h 2529
 
4.2%
c 2312
 
3.8%
Other values (8) 8908
14.7%
Uppercase Letter
ValueCountFrequency (%)
P 2214
22.6%
A 2000
20.4%
B 1718
17.5%
F 1145
11.7%
S 1016
10.4%
C 673
 
6.9%
L 357
 
3.6%
T 314
 
3.2%
E 248
 
2.5%
M 115
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 70430
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 9728
13.8%
e 8695
12.3%
r 7021
 
10.0%
i 5541
 
7.9%
n 5266
 
7.5%
a 4462
 
6.3%
o 3230
 
4.6%
p 2938
 
4.2%
h 2529
 
3.6%
c 2312
 
3.3%
Other values (18) 18708
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 9728
13.8%
e 8695
12.3%
r 7021
 
10.0%
i 5541
 
7.9%
n 5266
 
7.5%
a 4462
 
6.3%
o 3230
 
4.6%
p 2938
 
4.2%
h 2529
 
3.6%
c 2312
 
3.3%
Other values (18) 18708
26.6%
Distinct1849
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:42.868779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length127
Median length78
Mean length36.905714
Min length5

Characters and Unicode

Total characters361676
Distinct characters85
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)1.0%

Sample

1st rowBush Somerset Collection Bookcase
2nd rowHon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back
3rd rowSelf-Adhesive Address Labels for Typewriters by Universal
4th rowBretford CR4500 Series Slim Rectangular Table
5th rowEldon Fold 'N Roll Cart System
ValueCountFrequency (%)
xerox 844
 
1.5%
x 682
 
1.2%
with 589
 
1.1%
581
 
1.1%
avery 550
 
1.0%
for 527
 
1.0%
binders 512
 
0.9%
chair 468
 
0.9%
black 419
 
0.8%
phone 368
 
0.7%
Other values (2797) 49379
89.9%
2024-01-19T09:52:43.294153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44744
 
12.4%
e 32886
 
9.1%
r 20404
 
5.6%
o 19493
 
5.4%
a 18688
 
5.2%
i 18301
 
5.1%
l 16050
 
4.4%
n 15291
 
4.2%
s 14390
 
4.0%
t 14283
 
3.9%
Other values (75) 147146
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 233601
64.6%
Uppercase Letter 55190
 
15.3%
Space Separator 45159
 
12.5%
Decimal Number 17618
 
4.9%
Other Punctuation 6992
 
1.9%
Dash Punctuation 2872
 
0.8%
Final Punctuation 67
 
< 0.1%
Open Punctuation 60
 
< 0.1%
Close Punctuation 60
 
< 0.1%
Math Symbol 33
 
< 0.1%
Other values (2) 24
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 32886
14.1%
r 20404
 
8.7%
o 19493
 
8.3%
a 18688
 
8.0%
i 18301
 
7.8%
l 16050
 
6.9%
n 15291
 
6.5%
s 14390
 
6.2%
t 14283
 
6.1%
c 8717
 
3.7%
Other values (18) 55098
23.6%
Uppercase Letter
ValueCountFrequency (%)
S 6180
 
11.2%
C 5896
 
10.7%
B 5411
 
9.8%
P 4823
 
8.7%
D 2888
 
5.2%
A 2885
 
5.2%
M 2816
 
5.1%
T 2566
 
4.6%
F 2456
 
4.5%
L 2233
 
4.0%
Other values (16) 17036
30.9%
Other Punctuation
ValueCountFrequency (%)
, 3045
43.5%
/ 1530
21.9%
" 1272
18.2%
. 457
 
6.5%
& 277
 
4.0%
' 250
 
3.6%
# 89
 
1.3%
% 44
 
0.6%
! 9
 
0.1%
* 8
 
0.1%
Other values (2) 11
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 3695
21.0%
0 2858
16.2%
2 2227
12.6%
4 1702
9.7%
3 1501
8.5%
5 1417
 
8.0%
8 1227
 
7.0%
9 1212
 
6.9%
6 924
 
5.2%
7 855
 
4.9%
Space Separator
ValueCountFrequency (%)
44744
99.1%
  415
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 2872
100.0%
Final Punctuation
ValueCountFrequency (%)
67
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Math Symbol
ValueCountFrequency (%)
+ 33
100.0%
Initial Punctuation
ValueCountFrequency (%)
19
100.0%
Other Number
ValueCountFrequency (%)
¾ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 288791
79.8%
Common 72885
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 32886
 
11.4%
r 20404
 
7.1%
o 19493
 
6.7%
a 18688
 
6.5%
i 18301
 
6.3%
l 16050
 
5.6%
n 15291
 
5.3%
s 14390
 
5.0%
t 14283
 
4.9%
c 8717
 
3.0%
Other values (44) 110288
38.2%
Common
ValueCountFrequency (%)
44744
61.4%
1 3695
 
5.1%
, 3045
 
4.2%
- 2872
 
3.9%
0 2858
 
3.9%
2 2227
 
3.1%
4 1702
 
2.3%
/ 1530
 
2.1%
3 1501
 
2.1%
5 1417
 
1.9%
Other values (21) 7294
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 361153
99.9%
None 437
 
0.1%
Punctuation 86
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44744
 
12.4%
e 32886
 
9.1%
r 20404
 
5.6%
o 19493
 
5.4%
a 18688
 
5.2%
i 18301
 
5.1%
l 16050
 
4.4%
n 15291
 
4.2%
s 14390
 
4.0%
t 14283
 
4.0%
Other values (69) 146623
40.6%
None
ValueCountFrequency (%)
  415
95.0%
é 14
 
3.2%
¾ 5
 
1.1%
à 3
 
0.7%
Punctuation
ValueCountFrequency (%)
67
77.9%
19
 
22.1%

Sales
Real number (ℝ)

Distinct5757
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.76906
Minimum0.444
Maximum22638.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:43.691184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.444
5-th percentile4.9598
Q117.248
median54.49
Q3210.605
95-th percentile959.984
Maximum22638.48
Range22638.036
Interquartile range (IQR)193.357

Descriptive statistics

Standard deviation626.65187
Coefficient of variation (CV)2.7154935
Kurtosis304.44509
Mean230.76906
Median Absolute Deviation (MAD)45.463
Skewness12.983483
Sum2261536.8
Variance392692.57
MonotonicityNot monotonic
2024-01-19T09:52:43.881414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.96 55
 
0.6%
15.552 39
 
0.4%
19.44 39
 
0.4%
10.368 35
 
0.4%
25.92 34
 
0.3%
32.4 28
 
0.3%
17.94 21
 
0.2%
20.736 19
 
0.2%
6.48 19
 
0.2%
14.94 17
 
0.2%
Other values (5747) 9494
96.9%
ValueCountFrequency (%)
0.444 1
 
< 0.1%
0.556 1
 
< 0.1%
0.836 1
 
< 0.1%
0.852 1
 
< 0.1%
0.876 1
 
< 0.1%
0.898 1
 
< 0.1%
0.984 1
 
< 0.1%
0.99 1
 
< 0.1%
1.044 1
 
< 0.1%
1.08 3
< 0.1%
ValueCountFrequency (%)
22638.48 1
< 0.1%
17499.95 1
< 0.1%
13999.96 1
< 0.1%
11199.968 1
< 0.1%
10499.97 1
< 0.1%
9892.74 1
< 0.1%
9449.95 1
< 0.1%
9099.93 1
< 0.1%
8749.95 1
< 0.1%
8399.976 1
< 0.1%

Quantity
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.790102
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:44.056338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2212373
Coefficient of variation (CV)0.58606267
Kurtosis1.9538619
Mean3.790102
Median Absolute Deviation (MAD)1
Skewness1.2707753
Sum37143
Variance4.9338953
MonotonicityNot monotonic
2024-01-19T09:52:44.213419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 2368
24.2%
2 2352
24.0%
5 1206
12.3%
4 1166
11.9%
1 878
 
9.0%
7 587
 
6.0%
6 570
 
5.8%
9 257
 
2.6%
8 252
 
2.6%
10 55
 
0.6%
Other values (4) 109
 
1.1%
ValueCountFrequency (%)
1 878
 
9.0%
2 2352
24.0%
3 2368
24.2%
4 1166
11.9%
5 1206
12.3%
6 570
 
5.8%
7 587
 
6.0%
8 252
 
2.6%
9 257
 
2.6%
10 55
 
0.6%
ValueCountFrequency (%)
14 27
 
0.3%
13 26
 
0.3%
12 23
 
0.2%
11 33
 
0.3%
10 55
 
0.6%
9 257
 
2.6%
8 252
 
2.6%
7 587
6.0%
6 570
5.8%
5 1206
12.3%

Discount
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15679796
Minimum0
Maximum0.8
Zeros4691
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2024-01-19T09:52:44.370450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q30.2
95-th percentile0.7
Maximum0.8
Range0.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.20682026
Coefficient of variation (CV)1.319024
Kurtosis2.3871896
Mean0.15679796
Median Absolute Deviation (MAD)0.2
Skewness1.6795044
Sum1536.62
Variance0.042774622
MonotonicityNot monotonic
2024-01-19T09:52:44.543062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 4691
47.9%
0.2 3590
36.6%
0.7 412
 
4.2%
0.8 297
 
3.0%
0.3 226
 
2.3%
0.4 203
 
2.1%
0.6 134
 
1.4%
0.1 93
 
0.9%
0.5 66
 
0.7%
0.15 51
 
0.5%
Other values (2) 37
 
0.4%
ValueCountFrequency (%)
0 4691
47.9%
0.1 93
 
0.9%
0.15 51
 
0.5%
0.2 3590
36.6%
0.3 226
 
2.3%
0.32 26
 
0.3%
0.4 203
 
2.1%
0.45 11
 
0.1%
0.5 66
 
0.7%
0.6 134
 
1.4%
ValueCountFrequency (%)
0.8 297
 
3.0%
0.7 412
 
4.2%
0.6 134
 
1.4%
0.5 66
 
0.7%
0.45 11
 
0.1%
0.4 203
 
2.1%
0.32 26
 
0.3%
0.3 226
 
2.3%
0.2 3590
36.6%
0.15 51
 
0.5%

Profit
Real number (ℝ)

Distinct7184
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.467205
Minimum-6599.978
Maximum8399.976
Zeros62
Zeros (%)0.6%
Negative1847
Negative (%)18.8%
Memory size76.7 KiB
2024-01-19T09:52:44.732171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-6599.978
5-th percentile-53.78352
Q11.7045
median8.5556
Q329.3412
95-th percentile169.24846
Maximum8399.976
Range14999.954
Interquartile range (IQR)27.6367

Descriptive statistics

Standard deviation236.01112
Coefficient of variation (CV)8.290632
Kurtosis393.12161
Mean28.467205
Median Absolute Deviation (MAD)10.7726
Skewness7.5306034
Sum278978.61
Variance55701.249
MonotonicityNot monotonic
2024-01-19T09:52:44.981025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62
 
0.6%
6.2208 42
 
0.4%
9.3312 38
 
0.4%
5.4432 32
 
0.3%
3.6288 31
 
0.3%
15.552 26
 
0.3%
12.4416 20
 
0.2%
7.2576 19
 
0.2%
3.1104 16
 
0.2%
10.8864 10
 
0.1%
Other values (7174) 9504
97.0%
ValueCountFrequency (%)
-6599.978 1
< 0.1%
-3839.9904 1
< 0.1%
-3701.8928 1
< 0.1%
-3399.98 1
< 0.1%
-2929.4845 1
< 0.1%
-2639.9912 1
< 0.1%
-2287.782 1
< 0.1%
-1862.3124 1
< 0.1%
-1850.9464 1
< 0.1%
-1811.0784 1
< 0.1%
ValueCountFrequency (%)
8399.976 1
< 0.1%
6719.9808 1
< 0.1%
5039.9856 1
< 0.1%
4946.37 1
< 0.1%
4630.4755 1
< 0.1%
3919.9888 1
< 0.1%
3177.475 1
< 0.1%
2799.984 1
< 0.1%
2591.9568 1
< 0.1%
2504.2216 1
< 0.1%

Interactions

2024-01-19T09:52:34.245599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:29.382222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:30.403498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:31.331118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:32.227897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:33.260468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:34.409566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:29.555086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:30.551330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:31.472837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:32.369613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:33.418236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:34.566669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:29.712402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:30.701588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:31.630119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:32.653209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:33.574979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:34.724502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:29.869719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:30.858954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:31.771931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:32.793695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:33.731960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:34.884099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:30.074364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:31.016601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:31.913357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:32.953290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:33.906025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:35.039305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:30.251060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:31.160396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:32.070695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:33.118850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T09:52:34.064394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-19T09:52:35.242396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-19T09:52:35.478247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfit
01CA-2017-1521568/11/201711/11/2017Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420.0SouthFUR-BO-10001798FurnitureBookcasesBush Somerset Collection Bookcase261.960020.0041.9136
12CA-2017-1521568/11/201711/11/2017Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420.0SouthFUR-CH-10000454FurnitureChairsHon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back731.940030.00219.5820
23CA-2017-13868812/6/20176/16/2017Second ClassDV-13045Darrin Van HuffCorporateUnited StatesLos AngelesCalifornia90036.0WestOFF-LA-10000240Office SuppliesLabelsSelf-Adhesive Address Labels for Typewriters by Universal14.620020.006.8714
34US-2016-10896611/10/201610/18/2016Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311.0SouthFUR-TA-10000577FurnitureTablesBretford CR4500 Series Slim Rectangular Table957.577550.45-383.0310
45US-2016-10896611/10/201610/18/2016Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311.0SouthOFF-ST-10000760Office SuppliesStorageEldon Fold 'N Roll Cart System22.368020.202.5164
56CA-2015-1158129/6/20156/14/2015Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032.0WestFUR-FU-10001487FurnitureFurnishingsEldon Expressions Wood and Plastic Desk Accessories, Cherry Wood48.860070.0014.1694
67CA-2015-1158129/6/20156/14/2015Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032.0WestOFF-AR-10002833Office SuppliesArtNewell 3227.280040.001.9656
78CA-2015-1158129/6/20156/14/2015Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032.0WestTEC-PH-10002275TechnologyPhonesMitel 5320 IP Phone VoIP phone907.152060.2090.7152
89CA-2015-1158129/6/20156/14/2015Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032.0WestOFF-BI-10003910Office SuppliesBindersDXL Angle-View Binders with Locking Rings by Samsill18.504030.205.7825
910CA-2015-1158129/6/20156/14/2015Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032.0WestOFF-AP-10002892Office SuppliesAppliancesBelkin F5C206VTEL 6 Outlet Surge114.900050.0034.4700
Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfit
97909791CA-2018-1444913/27/20181/4/2018Standard ClassCJ-12010Caroline JumperConsumerUnited StatesHoustonTexas77070.0CentralFUR-CH-10001714FurnitureChairsGlobal Leather & Oak Executive Chair, Burgundy211.24620.3-66.3916
97919792CA-2015-1271665/21/20155/23/2015Second ClassKH-16360Katherine HughesConsumerUnited StatesHoustonTexas77070.0CentralOFF-EN-10003134Office SuppliesEnvelopesStaple envelope56.06460.221.0240
97929793CA-2015-1271665/21/20155/23/2015Second ClassKH-16360Katherine HughesConsumerUnited StatesHoustonTexas77070.0CentralFUR-CH-10003396FurnitureChairsGlobal Deluxe Steno Chair107.77220.3-29.2524
97939794CA-2015-1271665/21/20155/23/2015Second ClassKH-16360Katherine HughesConsumerUnited StatesHoustonTexas77070.0CentralOFF-PA-10001560Office SuppliesPaperAdams Telephone Message Books, 5 1/4” x 11”4.83210.21.6308
97949795CA-2015-1271665/21/20155/23/2015Second ClassKH-16360Katherine HughesConsumerUnited StatesHoustonTexas77070.0CentralOFF-BI-10000977Office SuppliesBindersIbico Plastic Spiral Binding Combs18.24030.8-31.0080
97959796CA-2017-1259205/21/20175/28/2017Standard ClassSH-19975Sally HughsbyCorporateUnited StatesChicagoIllinois60610.0CentralOFF-BI-10003429Office SuppliesBindersCardinal HOLDit! Binder Insert Strips,Extra Strips3.79830.8-5.8869
97969797CA-2016-12860812/1/20161/17/2016Standard ClassCS-12490Cindy SchnellingCorporateUnited StatesToledoOhio43615.0EastOFF-AR-10001374Office SuppliesArtBIC Brite Liner Highlighters, Chisel Tip10.36820.21.5552
97979798CA-2016-12860812/1/20161/17/2016Standard ClassCS-12490Cindy SchnellingCorporateUnited StatesToledoOhio43615.0EastTEC-PH-10004977TechnologyPhonesGE 30524EE4235.18820.4-43.1178
97989799CA-2016-12860812/1/20161/17/2016Standard ClassCS-12490Cindy SchnellingCorporateUnited StatesToledoOhio43615.0EastTEC-PH-10000912TechnologyPhonesAnker 24W Portable Micro USB Car Charger26.37640.42.6376
97999800CA-2016-12860812/1/20161/17/2016Standard ClassCS-12490Cindy SchnellingCorporateUnited StatesToledoOhio43615.0EastTEC-AC-10000487TechnologyAccessoriesSanDisk Cruzer 4 GB USB Flash Drive10.38420.22.2066